Project Presentation Ioannis Ioakeimidis, February 2014

SPLENDID aims to develop a technological system to help measure, evaluate and offer feedback about eating and activity behaviour in real-life. Aims

SPLENDID will offer personalized services guiding adolescents and young adults to adopt healthy eating and activity behaviors, preventing the onset of obesity and eating disorders

Key facts The idea behind SPLENDID was submitted to EU as a 3-year project at January 2013

Key facts The idea behind SPLENDID was submitted to EU as a 3-year project at January 2013 SPLENDID ranked 8 th out of 254 applications The project has been selected for receiving total funding of 2.7(3.5) M€ under the EU call: Personalized Guidance Services for lifestyle management and disease prevention SPLENDID officially initiated 1rst October 2013

Key facts The idea behind SPLENDID was submitted to EU as a 3-year project at January 2013 SPLENDID ranked 8 th out of 254 applications The project has been selected for receiving total funding of 2.7(3.5) M€ under the EU call: Personalized Guidance Services for lifestyle management and disease prevention SPLENDID officially initiated 1rst October 2013

Key facts The idea behind SPLENDID was submitted to EU as a 3-year project at January 2013 SPLENDID ranked 8 th out of 254 applications The project has been selected for receiving total funding of 2.7(3.5) M€ under the EU call: Personalized Guidance Services for lifestyle management and disease prevention SPLENDID officially initiated 1rst October 2013

The approach The system has three main components: A. Measuring behaviour B. Evaluating behavioural measurements C. Modifying behaviour through feedback

The approach Measurements of eating and activity behavior in real life conditions

The approach A plate with an attached scale measuring the rate of food consumption during a meal, i.e., the Mandometer Measurements of eating and activity behavior in real life conditions

The approach A plate with an attached scale measuring the rate of food consumption during a meal, i.e., the Mandometer A chewing sensor measuring chewing during day Measurements of eating and activity behavior in real life conditions

The approach A plate with an attached scale measuring the rate of food consumption during a meal, i.e., the Mandometer A chewing sensor measuring chewing during day A wearable activity meter recording body motion during day Measurements of eating and activity behavior in real life conditions

The approach A plate with an attached scale measuring the rate of food consumption during a meal, i.e., the Mandometer A chewing sensor measuring chewing during day A wearable activity meter recording body motion during day Subjective logs for fullness, intake and daily activities Measurements of eating and activity behavior in real life conditions

The approach A pipeline of algorithms

raw measurements pre-processing (e.g., correct meal mistakes) feature extraction (e.g., detect chewing events) indicators quantification (e.g., detect an ongoing meal based on chewing events) assessment of risk for obesity and eating disorders based on eating and activity behaviour patterns and personal profile data The approach A pipeline of algorithms

raw measurements pre-processing (e.g., correct meal mistakes) feature extraction (e.g., detect chewing events) indicators quantification (e.g., detect an ongoing meal based on chewing events) assessment of risk for obesity and eating disorders based on eating and activity behaviour patterns and personal profile data The approach A pipeline of algorithms

raw measurements pre-processing (e.g., correct meal mistakes) feature extraction (e.g., detect chewing events) indicators quantification (e.g., detect an ongoing meal based on chewing events) assessment of risk for obesity and eating disorders based on eating and activity behaviour patterns and personal profile data The approach A pipeline of algorithms

raw measurements pre-processing (e.g., correct meal mistakes) feature extraction (e.g., detect chewing events) indicators quantification (e.g., detect an ongoing meal based on chewing events) assessment of risk for obesity and eating disorders based on eating and activity behaviour patterns and personal profile data The approach A pipeline of algorithms

The approach Normalizing eating and physical activity behaviour

Use a Smartphone Development of a professional Portal for evaluation of measurements and personalized behavioural goal setting Development of user-friendly system for delivering behavioural feedback (real-time and report- based) to the user, based on the behavioural goal set by the health professional The approach Normalizing eating and physical activity behaviour

Use a Smartphone Development of a professional Portal for evaluation of measurements and personalized behavioural goal setting Development of user-friendly system for delivering behavioural feedback (real-time and report- based) to the user, based on the behavioural goal set by the health professional Normalizing eating and physical activity behaviour The approach

Use a Smartphone Development of a professional Portal for evaluation of measurements and personalized behavioural goal setting Development of user-friendly system for delivering behavioural feedback (real-time and report- based) to the user, based on the behavioural goal set by the health professional Normalizing eating and physical activity behaviour The approach

Correlate patterns detected on specific recordings of human generated signals to the risk of the development or aggravation of eating disorders and obesity –Eating style during meals –Daily physical activity patterns –Meal scheduling Automatic evaluation of how close the close the subjects are to reaching their goals –Ontology –Monitoring Scientific objectives

Correlate patterns detected on specific recordings of human generated signals to the risk of the development or aggravation of eating disorders and obesity –Eating style during meals –Daily physical activity patterns –Meal scheduling Automatic evaluation of how close the close the subjects are to reaching their goals –Ontology –Monitoring Scientific objectives

Correlate patterns detected on specific recordings of human generated signals to the risk of the development or aggravation of eating disorders and obesity –Eating style during meals –Daily physical activity patterns –Meal scheduling Automatic evaluation of how close the close the subjects are to reaching their goals –Ontology –Monitoring Scientific objectives

Technological objectives

Design and integration of sensors that capture eating behaviour and activity patterns Design and implementation of a personalised monitoring and guidance platform Technological objectives

Design and integration of sensors that capture eating behaviour and activity patterns Design and implementation of a personalised monitoring and guidance platform

Indented uses

As a school screening programme (adolescents) As a consumer lifestyle management service (young adults)

Indented uses As a school screening programme (adolescents) As a consumer lifestyle management service (young adults)

v1 Pilot 1 1st workshop Final Workshop v2 v3 Pilot 2 First Sensor Prototypes V1: Non-integrated version V2: 1st integrated prototype V3: Final version Sensor testing & data aquisition Workplan in a glance

Consortium

Contact Project Coordinator Prof. Anastasios Delopoulos Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece Dissemination Leader Cecilia Bergh, PhD Mando Group AB, Stockholm, Sweden

Thank you!!!

Mandometer Smartphone Activity-meter Self-rating logs Chewing sensor End user Website Database Professional Website